Machine learning-based system for sandboxing and malware detection

Abstract

A system for detecting and containing malicious software behavior using machine learning in a secure sandbox environment, consisting of: a sandbox controller configured to instantiate at least one virtualized execution environment (VEE) for executing an observed target software example; a behavioral feature extraction processing unit operatively coupled to the sandbox controller, the behavioral feature extraction processing unit configured to monitor and record system-level events, including system calls, file system interactions, memory allocation patterns, interprocess communication events, and network activity generated by the target software sample within the VEE; a machine learning inference engine operatively connected to the behavioral feature extraction processing unit, the inference engine comprising a plurality of trained models, including at least one deep neural network (DNN), a long short-term memory network (LSTM), and a gradient boosting decision tree (GBDT), each configured to generate a classification output based on an input behavior vector derived from the monitored system-level events; a meta-classification arbitration processing unit configured to combine the classification outputs of the plurality of trained models and generate a threat classification score indicating the maliciousness of the target software sample; a federated learning interface configured to securely transmit anonymized behavior vectors and receive global model updates from a distributed group of peer systems participating in a federated threat intelligence network; and An automated remediation control unit configured to initiate containment and response actions based on the threat classification assessment. These actions include file quarantine, virtual environment rollback, execution blocking, and forensic logging.

Description

FIELD OF THE INVENTIONThe present invention relates generally to the field of cybersecurity, and more particularly to a system and apparatus for detecting, classifying, and containing malware threats using machine learning techniques in a secure sandbox environment. The invention provides a smart framework for behavior analysis of potentially malicious software in real-time, and through dynamic execution and adaptive classification, enables precise identification of zero day threats, polymorphic malware, and advanced persistent threshs (APTs).BACKGROUND OF THE INVENTIONConventional antivirus and malware detection systems are strongly based on static analyses and signature-based detection techniques. Although effective against known malware, such systems are inadequate in modern threats such as obfuscated, polymorphic, or zero day attacks designed to bypass known signatures. Sandboxing techniques have alternatively become established by executing suspect code in isolated environments to observe its behavior. However, most conventional sandboxing systems have performance limitations, are prone to bypass techniques, and lack flexibility.Moreover, existing machine learning approaches to malware detection often operate in isolation from the execution context. This results in high false positive rates and limited applicability in practice. Therefore, there is an urgent need for a smart, hardware-based sandboxing system that combines deep behavioral analyses with machine learning-based classification in real time and is integrated into a secure and modular device structure that enables automated response to threats.Cybersecurity has dramatically changed in recent years as malware attacks become more sophisticated, frequent and domestic. Malware, a "malicious software" caseword, encompasses a broad range of threats including viruses, trojaner, ransomware, spyware, worms, rootkits, and advanced persistent threshs (APTs). These threats aim to interfere with systems, steal confidential data, provide unauthorized access, or exploit vulnerabilities in computer infrastructures. As companies increasingly employ digital strategies, cloud services, and networked devices, traditional malware detection techniques are becoming increasingly deficient.Historically, signature-based analysis formed the corner pillar of malware detection. This method is based on maintaining a database with known malware fingerprints - unique binary patterns or hashes - which can be matched to incoming files. Signature-based recognition is efficient and lightweight, but its greatest weakness is its reactive nature. It can only detect already identified threats and is therefore ineffective against zero day malware, metamorphic code, or obfuscated payloads to escape the detection. The enormous spread of malware variants, which are often created using automated malware generation tools, requires a constant update of the signature databases. Even then they are always one step ahead of the attackers, leaving critical weak points open.To address the deficiencies of signature-based systems, static analysis techniques have been introduced. These methods analyze the structure, metadata, and code of a file without executing it. Static analyzers deconstruct bins to examine strings, control flowcharts, and function call patterns to identify potential threats. Static analyses, while providing deeper insights into code behavior and may reveal embedded malicious logic, also have significant limitations. Among the most important ones are inability to identify runtime behavior and context-specific operations, especially when malware uses obfuscating techniques such as packaging, encryption, or polymorphism. Many modern malware variants are designed to appear harmless in static form and to disclose their true behavior only when executed in a particular environment.In response, dynamic analyses and sandboxing become more established as more active approaches. A sandbox is an isolated virtualized environment in which suspect files can be securely executed. The goal is to observe the behavior of a program at runtime - by tracking system calls, registration changes, network communication, file system changes, and memory usage. These behavior patterns can then be analyzed to determine whether a file is malicious. Sandboxing marked a paradigm shift in malware detection and allowed security systems to analyze unknown threats without jeopardizing the host system. However, despite their promising, conventional sandboxing systems encounter some operational and architectural limitations.A major disadvantage of conventional sandboxing solutions is their susceptibility to bypass techniques. Malware authors have become now experts in detecting sandbox environments and adapting their behavior accordingly. Techniques such as checking for the presence of virtual machines, examining hardware configurations, detecting (or missing) user interactions, and time-based obscuring may result in malware being harmless during analysis. Some malware delays execution or remains inactive unless specific triggers such as keyboard inputs or an Internet connection are present. This katz and mouse game makes sandboxing less effective against well constructed or polymorphic threats.Performance and scalability are further important aspects. Conventional sandboxing may be resource intensive and often requires multiple virtual machines (VMs) to emulate various operating systems and configurations. Executing complex or large bins in these VMs results in high overhead in terms of memory, CPU load, and hard disk I / O. This slows analysis throughput and can result in bottleneck in real-time threat detection pipelines. Moreover, sandbox environments are often used as stand-alone or centralized systems. This means that the findings obtained in an environment cannot be easily shared across distributed security infrastructures, which restricts collaborative learning and adaptation.Machine learning (ML) has gained importance in malware detection in recent years because it can detect patterns that cannot be so easily detected by rule-based systems. ML models can be trained using comprehensive malicious and harmless behavior datasets to learn distinctive features. These models can generalize from observed samples to identify novel threats, thus overcoming one of the greatest limitations of signature-based systems. However, many existing malware detection ML-based approaches are static: they analyze file features, strings, or entropy values before execution, rather than observing real-time behavior. Therefore, they have many of the same weaknesses as conventional static analyzers in terms of fogging and polymorphism.Moreover, ML-based solutions that take dynamic behavior into account are often based on offline processing. That is, features extracted from sandbox executions are sent to central servers where models perform inferences. This results in latency and prevents real-time responses that are critical in enterprise or high-security environments. The effectiveness of ML models also depends strongly on the quality and variety of training data. Many implementations suffer from record distortion, insufficient generalization, or overfitting of known malware examples. In addition, these systems rarely provide transparency in decision making, making it difficult for safety analyzers to interpret results or take targeted actions based on the model results.Another significant limitation of current solutions is the lack of integration into more comprehensive threat information ecosystems. Some sandboxing tools, while allowing limited exchange of threat signatures or compromise indicators (IOCs), are rarely involved in federated real-time learning environments. Without this functionality, systems remain isolated and miss the ability to learn from new threats of other nodes or organizations. Integration attempts are made more difficult by data protection concerns, regulations for data localization and the absence of secure, data protection-compliant collaboration protocols.Moreover, many existing solutions do not provide robust remedial and responsive mechanisms. Identification of a threat is only a partial aspect-equally important is the effective response thereto. Conventional tools are often limited to generating alerts and leave the study and response of human analytics. In high volume data environments, this results in warning fatigue and delayed damage limitation. Automated response mechanisms such as file quarantines, endpoint isolation, or dynamic policy enforcement are missing or inflexible, leading to operational inefficiencies and potentially false positive results.The physical architecture of conventional sandboxing systems also has limitations. Many are used as purely software systems on standard VMs and do not have hardware-supported security functions. As a result, they are susceptible to hypervisor escape attacks, BIOS-level root kits or supply chain attacks. Moreover, such implementations may not be well scalable in edge environments and do not support field-level security where the infrastructure may be constrained or unavailable.In view of these limitations, there is an urgent need for a new generation of malware detection systems that incorporate dynamic behavior sandboxing, real-time machine learning, and federated information exchange into a secure, modular hardware system. Such systems must be able to withstand avoidance maneuvers, scaleable in distributed environments, and both proactively detect and automatically react. They must integrate clearable AI mechanisms to assist human analytics in interpreting model decisions and employ privacy-friendly techniques to enable collaborative learning without jeopardizing sensitive data. These advances represent the next stage of smart cyber security defense and allow companies to always be one step ahead of increasingly adaptive and hard to grasp malware threats.SUMMARY OF THE INVENTIONThe invention disclosed herein includes a machine learning-based malware sandboxing and recognition system. It is comprised of secure hardware integrated with a dynamic execution engine, a behavioral feature extraction processing unit, a machine learning inference module, and a federated threat information interface. The system executes untrusted code examples in a containerized virtualized environment and monitors their behavior in real-time using high-precision telemetry. Behavioral characteristics are extracted, encoded, and passed through a multi-modal machine for machine learning that classifies the high-accuracy examples as harmless, suspect, or malicious.The device has a secure tamper resistant package with dedicated virtualization hardware, embedded GPUs, field programmable gate arrays (FPGAs), and secure boot mechanisms. The system also includes an automated remedial and responsive module that enables real-time countermeasures and forensic logging based on the threat classification.By integrating federated learning, differential privacy, and sharing behavioral fingerprinting, the system supports collaborative learning across distributed sandboxing nodes without jeopardizing local data security. This invention greatly improves the accuracy of threat detection, shortens the response time, and increases the resistance to advanced bypass techniques.The primary object of the present invention is to provide a robust, smart and secure malware detection system that overcomes the limitations of conventional signature-based, static and isolated dynamic analysis techniques. It is an object of the invention to develop an advanced machine learning-based malware sandboxing and recognition framework that can effectively identify and classify known and unknown malware by dynamic behavior analysis in real time. By integrating a hardware-based sandbox environment with a multi-modal machine learning inference engine, the invention ensures that malware with alternate or polymorphic behavior can be accurately profiled based on runtime characteristics and not based on superficial code patterns.Another object of the invention is to provide a scalable and modular device that can provide multiple virtualized execution environments in parallel, each tailored to emulation different system configurations and user behaviors. This allows a more comprehensive analysis of suspect files or processes in different execution contexts and thus increases the probability of discovering deeply rooted or conditionally activated malicious behavior. The invention also aims to prevent sandbox recognition and bypassing techniques employed by sophisticated malware through the use of enhanced virtualization layers, behavior simulation mechanisms, and tamperproof hardware designs.Another object of the invention is to integrate a high resolution behavior feature extraction subsystem that can synchronously detect near-system events such as system calls, registration interactions, network packets, storage usage, and file system changes. These behavioral signatures are converted to structured data representations and analyzed using ensemble-based machine learning models, including deep learning and recurrent neural networks, to enable precise real-time classification with minimal false alarms.Another object of the invention is to enable real-time exchange of threat data over a federated learning framework. This allows multiple sandbox devices in different networks or organizations to collectively train and update machine learning models without exchanging raw data. This maintains privacy while improving collective threat detection. The invention utilizes secure computational techniques such as homomorphic encryption and differential privacy to ensure that sensitive or protected information is not compromised during joint operations.Another object is to provide an integrated automated correction and reaction module that can take adaptive countermeasures upon detection of malicious activities. These responses may include isolation of virtual machines, removal of access rights, blocking of malicious IP addresses, notification of external SIEM systems, or quarantines of files. The aim is to minimize the need for manual interventions and to shorten the mean reaction time (MTTR) in the case of safety incidents.Moreover, the invention provides a comprehensive forensic logging and analysis framework that enables detailed analyses by events. Each execution session in the sandbox is recorded and securely stored with time stamped protocols, visualizations of behavioral plots, classification scores, and remedial action to ensure compliance with legal regulations, checking incidents, or future model schools.The disclosure relates to providing a next generation malware detection and repulsion device that is safe, intelligent, conformable, and comprehensible. It combines the strengths of virtualization, behavioral analysis, artificial intelligence, and federated threat reconnaissance in a unified, physically secure device that can be seamlessly deployed in enterprise networks, cloud infrastructures, or edge computing environments. This addresses the urgent need for proactive and autonomous cyber-defense mechanisms in view of rapidly developing and increasingly domestic malware threats.BRIEF DESCRIPTION OF THE FIGUREThese and other features, aspects and advantages of the present invention will become more fully understood by reading the following detailed description when taken in conjunction with the accompanying drawings, in which like numerals represent like parts throughout. The following applies here: FIG. 1 shows a block diagram of a machine learning based sandbox and malware detection.Those skilled in the art will also appreciate that the elements in the drawing are shown for simplicity and are not necessarily to scale. For example, the flowcharts illustrate the method using the key steps to improve understanding of aspects of the present disclosure. Also, as for the construction of the apparatus, individual or plural components of the apparatus may be represented by conventional symbols in the drawing. The drawing may only show the specific details relevant to understanding the embodiments of the present disclosure so as not to obscure the drawing with details readily apparent to those skilled in the art after the present description.DETAILED DESCRIPTION OF THE INVENTIONIn order to promote an understanding of the principles of the invention, reference will now be made to the embodiment illustrated in the drawings and will be described in an comprehensible manner. However, the scope of the invention is not limited thereby. Changes and further modifications of the illustrated system, as well as further applications of the principles of the invention, are possible, as would normally occur to a person skilled in the art.It will be understood by those skilled in the art that the foregoing general description and the following detailed description are exemplary and explanatory of the invention and are not intended to be limiting thereof.References throughout this specification to "one aspect," "another aspect," or similar language mean that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the phrases "in one embodiment," "in another embodiment," and similar phrases in this specification may or may not refer to the same embodiment.The terms "comprises," "comprising," or other variations thereof are intended to cover a non-exclusive inclusion, such that a process or method comprising a list of steps may include not only those steps, but also other steps not expressly listed or inherent in that process or method. Likewise, the phrase "comprises... for" one or more devices, subsystems, elements, structures, or components does not exclude, without further limitations, the existence of other devices, subsystems, elements, structures, components, or additional devices, subsystems, elements, structures, or components.Unless otherwise defined, all technical and scientific terms used herein have the same meaning as understood by one of ordinary skill in the art. The systems, methods, and examples provided herein are for illustrative purposes only and are not to be considered limiting.Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings.Referring now to FIG. 1, a block diagram of a machine learning based sandbox environment and detection is shown. The system 100 includes: a sandbox controller (102) configured to instantiate at least one virtualized execution environment (VEE) for executing an observed target software instance; a behavioral feature extraction processing unit (104) operatively coupled to the sandbox controller and configured to monitor and record system-level events including system calls, file system interactions, memory allocation patterns, interprocess communication events, and network activity generated by the target software sample within the VEE; a machine learning inference engine (106) operatively connected to the behavior feature extraction processing unit, the inference engine (106a) comprising a plurality of trained models including at least a deep neural network (DNN), a long short-term memory network (LSTM), and a gradient boosting decision tree (GBDT) each configured to generate a classification output based on an input behavior vector derived from the monitored system-level events; a metaclassification arbitration processing unit (108) configured to combine classification outputs of the plurality of trained models and generate a threat classification value indicative of the maliciousness of the target software example; a federated lemm interface (110) configured to securely transmit anonymous behavioral vectors and receive global model updates from a distributed group of peer systems participating in a federated threat reconnaissance network; and an automated correction controller (112) configured to initiate containment and response actions based on the threat classification value, these actions including file quarantines, virtual environment rollback, execution blocking, and forensic logging.The components described in the system for detecting and rejecting malicious software behavior are implemented primarily as hardware components, which are each implemented as separate physical processing or control units within a secure computer infrastructure. The sandbox controller acts as a dedicated hardware module that is responsible for managing and orchestrating virtualized execution environments (VEEs) and ensures the secure and isolated execution of target software examples. The behavioral characteristic extraction processing unit is a specialized hardware processor operatively connected to the sandbox controller to acquire, log, and preprocess system-level real-time events such as storage operations, file accesses, and network activity. The machine learning inference engine is a hardware accelerated module with embedded processing cores or AI-specific accelerators developed for executing complex trained models such as DNNs, LSTMs, and GBDTs with high efficiency and low latency. The metaclassification processing unit is dedicated hardware component for decision processing that integrates the results of the inference engine to determine a uniform threat classification value. The federated learning interface is implemented using secure hardware communication modules and enables encrypted and anonymous exchange of behavioral data and model updates with peer systems in a federated network. The automated mediation control unit is a hardware-based control logic module that performs containment and recovery operations in response to malicious behavior, thus ensuring fast and autonomous system protection. Together, these components form an integrated hardware framework that is optimized for real-time malware detection and containment in secure computing environments.In one embodiment, the sandbox controller (102) is also configured to instantiate multiple VEEs in parallel, each pre-installed with different operating system images, kernel versions, and simulated user activity scripts to mimic real execution conditions, and wherein the VEEs are dynamically destroyed and re-created after completion of each analysis session to prevent contamination and ensure environment integrity.In one embodiment, the behavior feature extraction processing unit (104) comprises a kernel mode driver configured to intercept system calls using a dynamic instrumentation framework, the framework inserting breakpoints and probes into user and kernel address spaces at runtime to track command-level behavior, which is then aggregated into temporal behavior diagrams for subsequent analysis.In one embodiment, the input behavior vector includes time-windowed features encoded as n-dimensional vectors representing event frequency, entropy, sequence order, statistical variance, timing between events, and API call coincidence matrices, and wherein the vector is normalized prior to inference and passed through a convolutional encoder.In one embodiment, the inference engine (106a) includes a self-attention mechanism integrated into the LSTM model to assign dynamic weights to feature subsequences indicative of abnormal behavior, wherein the attention level is trained to prioritize execution paths associated with privilege extension, legacy persistence, or code injection patterns.In one embodiment, federated lem interface (110) uses homomorphic lattice cryptography-based encryption techniques to enable aggregation of encrypted feature vectors and model gradient updates across federated nodes without decryption. The interface also applies differential data protection noise to ensure that the behavior of individual endpoints cannot be redeveloped from shared data.In one embodiment, the metaclassification arbitration processing unit (108) uses a weighted ensemble tuning strategy that includes dynamically tuned confidence thresholds for each model based on a feedback mechanism that re-weights the model impact over time as a function of the false positive and false negative rates observed in post-classification validation.In one embodiment, the automated correction controller (112) is also configured to generate a fixed audit log of all runtime activities, classification decisions, and response actions, where the log is cryptographically looked up and time stamped using a local blockchain ledger to ensure forensic integrity and audit traceability.In one embodiment, the sandbox controller (102) includes a hypervisor protection subsystem with hardware-based virtualization extensions, secure BIOS-level boot logic, and isolation of memory address space by enforcing nested page tables, the subsystem configured to resist a break-out from the hypervisor and installation of root kits during execution of unknown code examples.In one embodiment, the inference engine (106a) is configured to regularly retraining its models based on verified ground truth behavior datasets that were curved from previous sandbox sessions, wherein the retraining is performed in an isolated enclave processor such as Intel SGX or ARM TrustZone, and wherein the retraining includes techniques to improve robustness against attacks, including noise insertion and gradient concealing, to prevent model inversion attacks.The detailed description of the present invention relates to a machine learning-based malware sandboxing and recognition system. It utilizes advanced behavioral analyses, secure virtualized environments, and multi-modal artificial intelligence models for robust real-time classification and containment of malware. In essence, the system orchestrations the dynamic execution of unknown or untrusted software examples in strictly controlled and isolated virtualized execution environments (VEEs). Each of these environments is provided by a sandbox controller that provides various operating system images and emulates realistic user activities. These VEEs are instantiated and destroyed per analysis cycle to ensure environmental hygiene and eliminate the risk of cross-contamination by persistent malware residue.When using the target software example in a VEE, a processing unit for extracting behavioral characteristics (BFEU) is activated. The BFEU operates at kernel level using a dynamic instrumentation framework that inserts checkpoints and breakpoints into code paths in both user mode and kernel mode. This allows the system to acquire detailed telemetry data at the system level in real time. In particular, the BFEU monitors and logs low-level events, including system calls, registration and file system changes, patterns of memory allocation and release, process and thread creation, interprocess communication, and outgoing network activity. These events are time ordered and acquired with nanosecond precision and structured into both tabular feature vectors and directed temporal behavior graphs.The feature vectors generated by the BFEU encode complex relationships and patterns across different execution dimensions. For example, the system computes event co-occurrence matrices, transition probability distributions of API call sequences, temporal changes in memory entropy, and statistical measurements of I / O burst patterns. These raw features are then normalized and passed through a convolutional encoder layer to detect spatial correlations before being passed to the inference engine. In parallel, behavioral graphs representing the dynamic control flow of the sample also serve as inputs for graphable neural components of the system. This allows the model to detect structural anomalies such as unexpected branching behavior or unauthorized resource access.The inference engine consists of multiple machine learning models operating in parallel, each matched to a different aspect of behavior classification. These include a deep neural network (DNN) for static profile approximation, a long short-term memory (LSTM) recurrent neural network for temporal behavior modelling, and a gradient boosting decision tree (GBDT) ensemble for nonlinear feature interpretation. The LSTM model includes a self-attention mechanism that dynamically prioritizes feature subsequences that indicate hidden behavior, such as delayed loaded DLLs, stepwise code injections, and low noise privilege extensions. The DNN performs dimensional reduction on high resolution feature vectors, while the GBDT provides interpretable decision logic based on tree-based learning techniques that is ideally suited for conditional behavior pattern analysis.A metaclassification arbitration unit controls fusion of these multimodal inference results. This layer employs a dynamic weighting scheme based on continuous feedback of the results of the ground truth validation. The arbitration level calculates a final threat classification value that is calibrated over multiple confidence intervals and assigns a probability label that indicates whether the sample is harmless, suspect, or malignant. Moreover, this level supports class-by-class calibration for known malware families and can identify signatures of specific attack policies, techniques, and methods (TTPs).A particular feature of the system is its federated learning interface that enables secure and collaborative model refinement across multiple distributed nodes. Each participating node contributes anonymous behavior vectors and encrypted model updates to a central aggregator or peer-to-peer cluster. The system uses homomorphic encryption and secure aggregation protocols based on grid-based cryptography. These allow gradient updates to be combined without sacrificing the underlying data. Differential privacy noise is added to ensure that no conclusions can be drawn about individual behaviors or proprietary software features from federated contributions. This decentralized training paradigm allows the invention to adapt to new malware variants that occur worldwide and at the same time to maintain the data protection of individual organizations.In a positive malware classification, the system invokes the automated correction and response module (ARRM) that initiates a series of context-dependent containment actions. These actions include immediately terminating the VEE instance, file quarantines and hash locks, forwarding alerts to SIEM and SONAR platforms, and restoring system state to snapshotes prior to execution. The ARRM also archives a forensic package with the full execution history, behavior diagrams, feature vectors, classification metadata, and a signed correction log. This protocol is cryptographically mapped and stored in a local blockchain ledger to ensure immuncibility and non-dispensibility. This facilitates forensic investigations, compliance audits and the future adaptation of the machine learning models.To protect the system from manipulations and low-level attacks, the system is accommodated in a tamper-proof hardware system equipped with secure boot at the BIOS level, hardware-based virtualization functions (e.g., Intel VT-x, AMD-V) and nested page table isolation. The hypervisor is enhanced by microkernel level isolation and all inference operations are performed in a hardware-based secure enclave such as Intel SGX or ARM TrustZone to prevent data leaks over side channels or model extraction. In addition, the inference engine utilizes opposing training techniques including gradient masking, randomized smoothing, and input perturbation to protect against opposing malware samples that are to deceive the model.Finally, the system has a training pipeline that regularly updates its models using verified ground truth data from previous sandbox sessions. This training process is fully automated and occurs within the secure enclave. This enables continuous learning without data loss. New models are tested on retained validation sets and only deployed when they meet stringent performance benchmarks with respect to precision, call back, FI score, and ROC AUC metrics. This ensures that the system maintains high detection accuracy even when malware authors develop more and more alternate techniques.The machine learning-based malware sandboxing and recognition system includes a secure, modular construction that combines real-time execution analytics with smart malware classification mechanisms. Central component is a virtualized execution environment (VEE) controlled by a hardened hypervisor. This environment can create isolated containers that emulate various operating systems and kernel configurations. Suspect executable samples are introduced into these containers and executed there with accurate observation.A Behavioral Feature Extraction Processing Unit (BFEU) monitors execution features such as system calls, memory usage patterns, registration changes, file system interactions, API hook behavior, and outgoing network connections. These data points are time stamped, encoded into structured vectors, and stored in a system local behavior database. Each feature vector may also be converted to a behavior diagram for additional contextual analyses.The system's multi-modal machine learning engine (MMLE) includes a series of ensemble machine learning classifiers trained from datasets that include millions of harmless and malicious software behavior patterns. Models used include random forests, SVMs, deep neural networks (DNNs), convolutional neural networks (CNNs), and LSTM architectures. The MMLE is configured to perform real-time inferences with GPU acceleration and dynamically select the most suitable model based on the behavior graph and the feature complexity.An arbitration plane is integrated into the MMLE to consolidate classification results of different models. This level uses weighted confidence values and contextual anomaly detection metrics to achieve a final classification result. The arbitration logic is further refined by reference to a local three intelligence cache that is synchronized to external nodes via the Federal Three Intelligence Interface (FTII).The FTII supports federated learning by transmitting obfuscated behavioral fingerprints and anonymous feature vectors to other trusted sandbox environments. It uses homomorphic encryption and differential privacy to ensure that no sensitive data is lost during the synchronization process. Machine learning model updates are distributed over secure channels so that each device can learn from new threats in the network.The automated Rehabilitation and Reaction Module (ARRM) is activated when the classification indicates a malignant or suspect result. The ARRM may place the malicious payload under quarantines, isolate the infected container, log activity for forensic purposes, alert external systems via API integration, and optionally trigger network level defenses such as firewall rule updates or user access withdrawal.The device is accommodated in a tamper-proof, electromagnetically shielded housing. It has two-layer biometric access controls and secure boot logic controlled by a trusted platform module (TPM). The package is equipped with redundant power supplies, high speed data interfaces, and internal heat sinks. The active temperature control provides reliable 24 / 7 operation. An integrated management interface allows administrators to access via a secure local or remote console.In the operating mode, a suspect file is communicated to the system. In the event of a hash validation error, the file is stored in a newly created container in a sandbox. The BFEU monitors and extracts behavioral data during execution. These features are streamed to the MMLE for classification. In the case of malware, the ARRM is triggered and a complete forensic report is created and optionally transmitted to central logging systems. Simultaneously, FTII transmits the behavioral fingerprint to federated peers and thus updates the knowledge database of the system.This invention provides a pathbreaking solution in malware detection and containment by merging dynamic sandboxing, advanced behavioral analyses, machine learning, and federated information exchange into a unified, robust framework. It is particularly effective against difficult to grasp malware variants that bypass conventional defense mechanisms and offers a scalable way of autonomous cyber defense.The present invention relates generally to the field of cybersecure and malware detection, and more particularly to systems and methods for detecting, analyzing, and containing software-based threats using machine learning techniques in isolated sandbox environments. The invention falls within the fields of artificial intelligence, behavioral threat analysis, secure virtualization, and federated learning, and is particularly suitable for detecting advanced persistent threshs (APTs), zero day exploits, and polymorphic malware that escape conventional signature-based detection methods. It integrates real-time behavior profiling, deep learning based inference, and autonomous response mechanisms in a secure, hardware-based device and is therefore suitable for use in enterprise networks, cloud infrastructures, and edge computing environments that require robust, smart threat detection functions.The drawings and the foregoing description show examples of embodiments. Those skilled in the art will appreciate that one or more of the described elements may well be combined into a single functional element. Alternatively, certain elements may be divided into multiple functional elements. Elements of one embodiment may be added to another embodiment. For example, the order of the processes described herein may be changed and is not limited to the manner described herein. Moreover, the actions of a flow chart need not be performed in the order shown; nor do all actions necessarily need to be performed. Also, actions that are not dependent on other actions may be performed in parallel with the other actions. The scope of the embodiments is by no means limited by these specific examples. Numerous variations, whether or not explicitly stated in the specification, such as differences in structure, dimensions, and material use, are possible. The scope of the embodiments is at least as broad as recited in the following claims.Advantages, other advantages and solutions to problems have been described above with reference to specific embodiments. However, the advantages, merits, solutions to problems and any components that may result in an advantage, merit or solution being introduced or enhanced are not to be understood as critical, required or essential features or components of individual or all claims.REFERENCES100 Block Diagram of a Machine Learning Based Sandboxing and Malware Recognition System. 102 sandbox controller 104 processing unit for extraction of behavioral characteristics 106 inference engine for machine learning 106 a inferencing engine 108 meta-classification arbitration unit 110 interface for federated learning 112 automated remedial control unit

Claims

A system for detecting and containing malicious software behavior using machine learning in a secure sandbox environment, comprising: a sandbox controller configured to instantiate at least one virtualized execution environment (VEE) for executing an observed target software instance; a behavior feature extraction processing unit operatively coupled to the sandbox controller, wherein the behavior feature extraction processing unit is configured to monitor and record system-level events including system calls, file system interactions, memory allocation patterns, interprocess communication events, and network activity generated by the target software sample within the VEE; a machine learning inference engine operatively connected to the behavior feature extraction processing unit, the inference engine comprising a plurality of trained models including at least a deep neural network (DNN), a long short-term memory network (LSTM), and a gradient boosting decision tree (GBDT) each configured to generate a classification output based on an input behavior vector derived from the monitored system-level events; a metaclassification arbitration processing unit configured to combine the classification outputs of the plurality of trained models and generate a threat classification value indicative of the maliciousness of the target software sample; a federated lemm interface configured to securely transmit anonymous behavioral vectors and receive global model updates from a distributed group of peer systems participating in a federated threat reconnaissance network; and an automated remedial control unit configured to initiate dusitation and responsive actions based on the threat classification score. These actions include file quarantines, virtual environment rollback, execution blocking, and forensic logging.The system of claim 1, wherein the sandbox controller is further configured to instantiate multiple VEEs in parallel, each pre-installed with different operating system images, kernel versions, and simulated user activity scripts to mimic real execution conditions, and wherein the VEEs are dynamically destroyed and re-created after completion of each analysis session to prevent contamination and ensure environment integrity.The system of claim 1, wherein the behavior feature extraction processing unit comprises a kernel mode driver configured to intercept system calls using a dynamic instrumentation framework, the framework inserting breakpoints and probes into user and kernel address spaces at runtime to track command-level behavior, which is then aggregated into temporal behavior diagrams for subsequent analysis.The system of claim 1, wherein the input behavior vector comprises time-windowed features encoded as n-dimensional vectors representing event frequency, entropy, sequence order, statistical variance, timing between events, and API call co-occurrence matrices, and wherein the vector is normalized prior to inference and passed through a convolutional encoder.The system of claim 1, wherein the inference engine comprises a self-attention mechanism integrated into the LSTM model to assign dynamic weights to feature sub-sequences indicative of abnormal behavior, wherein the attention layer is trained to prioritize execution paths associated with privilege extension, legacy persistence, or code injection patterns.The system of claim 1, wherein the federated lem interface uses homomorphic lattice cryptography-based encryption techniques to enable aggregation of encrypted feature vectors and model gradient updates across federated nodes without decryption, and wherein the interface also applies differential data protection noise to ensure that the behavior of individual endpoints cannot be redeveloped from shared data.The system of claim 1, wherein the metaclassification unit uses a weighted ensemble vote that includes dynamically voted confidence thresholds for each model based on a feedback mechanism that reweighs the model impact over time as a function of the false positive and false negative rates observed in post-classification validation.The system of claim 1, wherein the automated correction controller is further configured to generate a fixed audit log of all runtime activities, classification decisions, and response actions, the log being cryptographically looked up and time stamped using a local blockchain ledger to ensure forensic integrity and audit traceability.

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